Dialog systems are systems in which a person speaks or otherwise enters input to a computer in natural language, in order to accomplish a result. With the rise of microprocessor-controlled appliances and equipment, dialog systems are becoming increasingly used to facilitate the man-machine interface in many applications such as computers, automobiles, industrial machinery, home appliances, automated telephone services, and so on. Dialog systems allow a user to enter a request or query in an expression or manner familiar to the user without requiring the user to learn a particular language or structure dictated by the computer. Dialog systems process the query and access one or more databases to retrieve responses to the query. In order to provide meaningful results with as little user interaction as possible, dialog systems should be designed and implemented to accommodate large variations in the content and format of the queries, as well as the content and format of the responsive data.
Current natural language dialog systems are typically data driven systems in which a query is processed and possible responses are retrieved from a database. The data driven system returns responses based on a distribution model that quantifies how close a response is to the query, based on a quantifiable metric, such as a key-word hit. These dialog systems may perform structured database queries, or unstructured database queries, however the results are represented in the same manner: as a list of items. Such current systems have several drawbacks. One critical drawback is that these systems often provide users with either no responses or too many responses. In both cases, the user effectively receives no meaningful information, since zero responses to a query or too many responses to a query can leave the user feeling confused or overwhelmed. Although these systems may provide some degree of user interaction to modify the query to obtain meaningful results, in many of these situations, users are required to recite specific phrasings, listen to lengthy prompts or otherwise conform their query to a format dictated by the system. This disadvantage of current dialog systems is especially critical when the users are preoccupied with other important tasks, such as driving a car or operating heavy machinery. For cognitively demanding situations such as driving in heavy traffic, it is very important to allow users to speak naturally and to allow flexibility in dialog flow. Because drivers are focusing on driving, and not on reading a graphical display, it is essential for the system content to be delivered in a way that does not overwhelm them or distract them from the task of driving.
An additional disadvantage of typical present dialog systems is that they rely on static rules, data structures and/or data content to process and return responses to user queries. These systems are often limited to a particular use or application, and cannot be easily customized differently for each application or service. Furthermore, such systems often do not dynamically account for, or modify, the processing method in response to user preferences, historical data, and other salient factors relevant in the query process.